|Veröffentlichungsdatum||4. Juni 2013|
|Eingetragen||11. Aug. 2003|
|Prioritätsdatum||11. Aug. 2003|
|Auch veröffentlicht unter||US20050038893, US20140006168|
|Veröffentlichungsnummer||10639811, 639811, US 8458033 B2, US 8458033B2, US-B2-8458033, US8458033 B2, US8458033B2|
|Ursprünglich Bevollmächtigter||Dropbox, Inc.|
|Zitat exportieren||BiBTeX, EndNote, RefMan|
|Patentzitate (82), Nichtpatentzitate (7), Referenziert von (2), Klassifizierungen (6), Juristische Ereignisse (6)|
|Externe Links: USPTO, USPTO-Zuordnung, Espacenet|
The invention generally relates to profiling offers and consumers, and using the profiles to determine or predict the relevance of the offers to consumers.
Merchants use various statistical methods and models in an attempt to maximize the effectiveness of offers sent to consumers. Certain of such methods and models attempt to predict the likelihood that a given consumer or a set of consumers will respond favorably to a particular offer based on various geographic and demographic characteristics of the consumer. Other models look at historical purchase or payment data of current customers to predict follow-on behavior such as the current customer's future purchases or payment behavior.
In general, the accuracy of any model is dependent on the amount, relevancy, and quality of the input data. A model that includes every piece of information about a person and all their actions could make very accurate predictions regarding the future behaviors of that person. However, the amount of computing resources, data, and time needed to create an all-inclusive model makes such a system cost prohibitive to build and to maintain. In contrast, “mass mailings” tend to have minimal up-front modeling costs because offers are sent out with little or no regard to the recipients. However, this approach can have success rates (number of purchases/number of offers sent) of less than 1%, and thus generally are not effective. Sending such a large volume of offers that are ultimately never acted upon can cost a significant amount of money, and produces minimal returns. In addition to the two extreme approaches described above, other methods attempt to achieve an economic balance between maximizing offer success rates and minimizing the complexity, and thus the cost, of the predictive models.
One approach is the use of “marketing databases.” In general, marketing databases store information about current and/or potential customers. Such information can include data regarding a customer's previous purchases, customer service interactions, promotions received by a customer, geographic data, and demographic data. As the database accumulates more information about current customers, merchants can build models with predictive features, including determining which consumers are more likely to respond to certain offers in a positive fashion. Another approach is collaborative filtering, where a merchant compares prior purchases of one consumer to the prior purchases of another consumers. When the one consumer revisits the merchant (for example, in person, on the World Wide Web, or by receiving a catalog) the merchant presents the one consumer with offers for products or services purchased by the other consumers who have purchase histories similar to those of the one customer. This approach requires that the merchant have some historical purchase information about the one consumer.
Merchants also track the behavior of web site visitors by assigning each user a unique identifier which can then be kept in the URL as the visitor visits other pages of the site or stored as a “cookie” file in the browser. By this means, merchants and advertisers can track the pages viewed and the products purchased of each user. Thus, the operator may target ads, offers, or content accordingly. This approach is also based on a consumer's previous interactions with the merchant.
Merchants can use information about a consumer's historical purchases in concert with generally available demographic data, but even such combinations do not allow the prediction with accuracy of the behavior of a consumer who has no prior interaction with the merchant.
Electronic communications companies provide a multitude of services to consumers and merchants such as Internet access, delivery of text and graphical content, web-based electronic mail (“email”), and instant messaging. Furthermore, these same companies provide merchant services such as advertisement development, content and application hosting, distribution of offers to members of the general public, and access to advertisement space on content pages and emails. By providing these services, the electronic communication companies have access to large amounts of communications sent to and from both their users as well as members of the general public, manage the delivery of electronic offers from merchants to general visitors to their sites, and track which visitors select particular advertisements. This large volume of data offers a significant opportunity to classify, analyze, and score the content of communications based on the actions of particular users in order to better predict the effectiveness of offers without requiring an initial interaction between the consumer and the merchants making the offers.
The invention relates to methods and systems for assessing the relevance of an offer to be presented to a consumer by applying profiling and classification methods to the content of communications among consumers. More particularly, the invention relates to targeting offers accurately to consumers by creating offer profiles based on the content of various communications associated with consumers who have responded to the offers but whose communications do not contain any reference to or information about the offers, creating consumer profiles based on the cumulative content of the communications associated with consumers, comparing a consumer's consumer profile to a set of offer profiles, determining an offer profile that tends to match the consumer's consumer profile, and delivering the offer associated with that profile to that consumer.
In one aspect, the invention relates to a method of determining the relevance of offers to consumers. The method comprises providing a plurality of offer profiles, each of which is associated with an offer and based at least in part on the content of at least one communication which is unrelated to the offer. Furthermore, the method comprises providing a plurality of consumer profiles. Each consumer profile is associated with a consumer and based on the content of at least one communication associated with the consumer. In addition, the method determines the relevance of one of the offers to one of the consumers by comparing the offer profile associated with that offer to the consumer profile associated with that consumer.
Prior to providing the offer profiles, the offers can be sent to at least one consumer. The at least one communication unrelated to the offer can be associated with the at least one consumer. Information about a consumer's responses to one the offers can be received. The offer profiles can be based, in part, on the information received about the response of one of the consumers to one of the offers, and a weight can be given to at least one communication unrelated to the offer based on the information received about the response of one of the consumers to one of the offers. At least one of the offers may be an advertisement. At least one of the communications unrelated to the offer can be an electronic communication. The electronic communication can be one or more of an electronic mail message, an instant message, or a voice message. At least one of the communications associated with a consumer can be an electronic communication. The electronic communication associated with a consumer can be one or more of an electronic mail message, an instant message, or a voice message.
Another aspect of the invention features a system for determining the relevance of offers to users of an electronic communications system. The system includes a first profiler for determining an offer profile for an offer based at least in part on the content of at least one communication unrelated to the offer. The system also includes a second profiler for determining a consumer profile for a user of the electronic communications system based at least in part on the content of electronic communications associated with the user of the electronic communications system. Furthermore, the system includes a comparison engine for comparing the offer profile to the consumer profile to determine the relevance of the offer to the user.
The system can include a delivery engine for delivering one or more offers to the users of the electronic communications system. The system can include a receiver for receiving information about at least one response by a user of the electronic communications system to the one or more offers delivered to the user of the electronic communications system. The first profiler may assign a weight to a communication unrelated to the offer based on the information about a response by a user of the electronic communications system to the one or more offers delivered to the user of the electronic communications system. The system may further include a database server for storing at least one of the offer, the offer profile, the consumer profile, the information about a response by a user of the electronic communications system to the one or more offers delivered to the user of the electronic communications system, and the electronic communication. The system may also include a communications server for facilitating communications among the users of the electronic communications system. In some versions of the invention, the first and second profilers are implemented as one software component.
Another aspect of the invention relates to a method of creating an offer profile. The method comprises associating with an offer at least one consumer communication unrelated to the offer. The method further comprises creating a profile for the offer based at least in part on the content of the at least one consumer communication unrelated to the offer.
The offer can be an advertisement. Prior to associating with an offer at least one consumer communication unrelated to the offer, the offer can be sent to at least one consumer. The at least one consumer communication can be associated with the at least one consumer. Prior to associating with an offer at least one consumer communication unrelated to the offer, information about a response to the offer by a consumer may be received. The offer profile can be based, at least in part, on the information received about a response to the offer by a consumer, and a weight may be given to at least one consumer communication unrelated to the offer based on the information received about a response to the offer by a consumer.
Yet another aspect of the invention relates to a method of creating a consumer profile. The method comprises assigning communications between two or more consumers to at least one of the two or more consumers. The method further comprises creating a consumer profile for at least one of the two or more consumers based at least in part on the content of the communications assigned to the at least one of the two or more consumers. The communications between two or more consumers can be electronic communications. Furthermore, the electronic communications can be one or more of an electronic mail message, an instant message, or a voice message.
The foregoing and other objects, aspects, features, and advantages of the invention will become more apparent from the following description and from the claims.
For example, the communication channels 116 can connect the clients, 102 to a local-area network (LAN), such as a company intranet, a wide area network (WAN) such as the Internet, and/or other such network 114. The communication channels 116 that allow the clients 102 and the servers 104, 106, 108, 110, and 112 to communicate with the network 114 through the communication channels 116 can be any of a variety of connections including, for example, standard telephone lines, LAN or WAN links (e.g., T1, T3, 56kb, X.25), broadband connections (ISDN, Frame Relay, ATM), and/or wireless connections. The connections can be established using a variety of communication protocols (e.g., HTTP(S), TCP/IP, SSL, IPX, SPX, NetBIOS, Ethernet, RS232, direct asynchronous connections, a proprietary protocol). In one embodiment, the clients 102 and the servers 104, 106, 108, 110, and 112 encrypt some or all communications when communicating with each other or other devices, not shown.
Each of the servers 104, 106, 108, 110, and 112 can be any computing device capable of providing the services requested by the clients 102, such as personal computers, personal data, assistants, and phones. This includes delivering content, media, communications services, and advertisements via the Internet and World Wide Web, as described in more detail below.
For purposes of illustration,
Each of the clients 102 can be any computing device (e.g., a personal computer, set top box, wireless mobile phone, handheld device, personal digital assistant, kiosk) used to provide a user interface to access the network 114 and the web server 104. The clients 102 can include one or more input/output devices such as a keyboard, a mouse, a screen, a touch-pad, a biometric input device, etc. The clients 102 also include an operating system such as any member of the WINDOWS family of operating systems from Microsoft Corporation, the MACINTOSH operating system from Apple Computer, and various varieties of Unix, such as SUN SOLARIS from SUN MICROSYSTEMS, and GNU/Linux from RED HAT, INC., for example. The clients 102 also can be implemented on such hardware as a smart or dumb terminal, network computer, wireless device, information appliance, workstation, minicomputer, mainframe computer, or other computing device, that is operated as a general purpose computer or a special purpose hardware device solely used for serving as a client 102 to access the network 114. The clients 102 also may include one or more client-resident applications, such as INTERNET EXPLORER developed by Microsoft Corporation or NAVIGATOR developed by AOL Time Warner Corporation.
As an illustration of how one embodiment of the invention may operate in the environment 100, a user of an electronic communications system (“user”) accesses the web server 104 from a client 102 a. In some embodiments, access to the web server 104 may be restricted, thus requiring the user to provide a form of personal identification such as a user identifier (“ID”), a password, a secure id, biometric information, or other authentication information uniquely associated with the user. In other embodiments, the user may be a member of the general public visiting a web site hosted on the web server 104 for general public use. The web server 104 may validate that the user has previously registered for the services offered by that web server 104. The web server then grants the user access to some or all of the contents web server 104, the mail server 106, or other services offered by the service. The user may then send and receive electronic communications via the web server 104, the mail server 106, or other communication devices not shown. Such communications may include email, instant messaging, file sharing, voice, video, photographs, audio files, etc. The mail server 106, or other modules used to facilitate communication among users of the service, associates each communication with one or users by assigning a communication to one or more of a sender, to one or more recipients, or to other users referenced in the communication. The mail server 106 then groups the communications by each user to which a communication is associated, and sends the groups of communications to the profile server 110. The profile server 110 then creates a unique consumer profile for one or more users based on the cumulative content of the communications associated with each user.
The content of the communications associated with a user includes the body text or any of the accompanying information related to an electronic message such as an email, an instant message, or a web log posting. The accompanying information can include information contained in an email header, information contained in the subject line, the addressees of the communication, attachments to the communication, the date the communication was sent, the time the communication was sent, the location from where the communication was sent, type of computer the communication was sent from, or type of service used to compose or deliver the communication.
The user also may request to see a particular page or pages of content from the World Wide Web. The web server 104 delivers the requested content to the client 102 a by receiving HTTP requests from the client 102 a, compiling the web page or pages from the content provider, and delivering the content to the client 102 a. Merchants or content providers may associate one or more offers with a page of content by including the offers for services or products with pages of content. The offers may be in the form of electronic advertisements, known as banner ads, pop-up ads, emails, or other similar offers for goods or services. The offers may be stored, for example, in an ad server 108, which may be managed by a network service provider, by a merchant, or by a third party. Each offer is assigned a unique offer identifier (“offer ID”) for use by the web server 104, the ad server 108, the profile server 110, and the merchant server 112. When a user requests a page of content having one or more offers associated with it, the web server 104 determines the offer ID(s) associated with the content page and queries the ad server 108 for the offer(s) identified by the offer ID(s). The web server 104 then sends one integrated Web page including both the requested content and the associated offer(s) to the client 102 a.
If a user wishes to inquire about one of the offers presented on the page of Web content or attached to an email in the email footer, the user may select the offer by, for example, navigating a screen pointer to the offer of interest and “clicking” on the offer. This action may open a new display window on the client 102 a with more detailed information from the merchant, replace information displayed in the current display window with information from the merchant, or alert the merchant to send additional information to the user. The user may then decide to initiate a transaction with the merchant, continue to browse the additional information provided by the merchant, or return to the originating content page or email. If the user performs some operation representing a meaningful event (such as making a purchase, or some other event as identified as relevant by the merchant), the merchant server 112 captures and stores information associated with the event. In some embodiments, a merchant may deem “no action” by the user as a meaningful event, and therefore captures the non-response of the user. Information associated with the event can include, for example, the amount paid by the user, the products purchased by the user, the user's name, the user's unique user ID, the unique offer identifier for the offer viewed, the date the offer was viewed, the time of day the offer was viewed, the amount of time the offer was viewed, etc. The merchant server 112 then forwards the event information to the profile server 110. The profile server 110 then requests, from the mail server 106 or other communications storage device, some or all of the communications associated with the user ID forwarded from the merchant server 112. The profile server 110 adds some or all of the content contained in the user communications received from the mail server 106 to a collection of the cumulative content of the communications from the other users, each of whom have viewed and responded to the particular offer. The profile server 110 then determines an “offer profile” based at least in part on the cumulative content of the communications.
To create offer profiles, the profile server 110 may use one or more of many document classification techniques known to those skilled in the art. Examples of such techniques include naive Bayesian, centroid-based, k nearest neighbors, CRM114, latent semantic analysis, as well as others.
Once the profile server 110 creates the offer profiles, the profile server 110 may compare offer profiles based on the content of communications from users who previously responded to particular offers to the consumer profiles of other users who request content pages to which the offers are associated. For example, one particular offer profile may show a statistical correlation to a consumer profile associated with a particular user. Thus, the offer associated with that offer profile may have more relevance to the user associated with the consumer profile than other offers. This correlation information can then be sent to the ad server 108, which can in turn determine which offer to show to the user requesting a page of Web content.
For example, where the offers are assigned in a many-to-many fashion, the merchant can assign multiple offers to an individual page of content such that a user will not see the same offer when viewing the same page multiple times. Furthermore, the merchant may assign one offer to multiple pages of content such that the same offer may appear on more than one page of content. As an illustration, an airline may create ten distinct offers for discount airfares to ten different destinations, and assign the offers to multiple Web pages within a travel information Web site. Therefore, the first time a user requests a page of information on the Caribbean, the user may see an offer to purchase a discounted airfare ticket to Puerto Rico, and the second time the user requests the same page, the offer may be for the Bahamas. The same user may subsequently request a Web page with information on cruise destinations, and may be shown the same offer for airfare to Puerto Rico.
Referring again to
The user also may request that one or more pages of content be delivered to the client 102 a by entering an HTTP address into a location field in an browser application, selecting a saved HTTP address from a list of previously visited locations, or selecting an HTTP link from a Web page. The web server 104 processes the request, and delivers the requested content (step 220) to the client 102 a, including offers such as banner advertisements, email footers, or other electronic advertisements as previously described. The user then may view the offers included on the content pages or emails (step 222). The user's response to the offer is then captured by the merchant server 112 (step 226). The user's response may also be captured by the web server 104 (step 224). The user's response to the offer may be any one of selecting one of the offers, not selecting an offer, purchasing a product, calling a phone number, sending an email, or other action deemed by the merchant to be relevant. The merchant server 112 then captures the user's response, and any information related to the user's response (step 226). Related data can include the user's name, the user's unique user ID, the amount the user spent, the products or services the user purchased, the length of time the user viewed the offer, the date the user viewed the offer, the time of date the user viewed the offer, etc.
Once the merchant server 112 determines that one or more users responded to an offer, the merchant server 112 sends the information relating to the response(s) to the profile server 110 (step 228). The merchant server 112 sends the user ID's for the users who responded to the offer, the offer ID of the offer to which the users responded, as well as other information related to the transaction(s). The profile server 110 then requests some or all of the communications associated with the users identified by the merchant server 112 (step 230) from the mail server 106. The mail server 106 then groups all the requested communications (step 232) and sends the collection of communications to the profile server 110 (step 234). The profile server 110 associates the collection of communications with the offer by matching the user IDs sent with the communications to the user IDs and offer IDs sent from the merchant server 112 (step 236). The profile server 110 then creates an offer profile for the offer based at least in part on the cumulative content of some or all the communications associated with the users who responded to the offer (step 238). In some embodiments, the profile server may wait a specified period of time before requesting the emails of the users who responded to the offers. By doing so, the profile server may have a greater number of emails from which to build the profile, or may be able to assign different weights to emails based on, for example, how recently the email was sent, or the particular content page to which the offer was assigned at the time user viewed the offer. Some merchants may not forward information about which users responded to a particular offer. In such cases, offer profiles for similar offers may be used as offer profiles for the particular offer. Similarities my be based, for example, on the product or service being offered, the price at which the product or service is being offered, as well as others.
In one version of the invention, the profile server 110 uses the data relating to the user's response to the offer to further determine the offer profile. For example, the profile server 110 may weight the content from communications sent or received during a particular period (just before viewing the offer, for example) more heavily than communications sent years before viewing the offer. In another example, communications associated with users who purchased goods or services based on viewing an offer may be weighted more heavily than those who only inquired about a product. In other examples, the importance of one subset of communications may be weighted more heavily than a second subset if the first subset of communications are associated with a user who spent over a certain amount of money with the merchant, while the second user did not.
In some embodiments, the determination of whether to use a particular set of communications, or the degree to which they are weighted may not occur instantaneously, and in fact may be determined over a period of time such as a year or more. For example, a credit card company may determine that the relevant information about a particular consumer's response to an offer to sign up for a credit card is not whether the consumer signed up for the card, but whether they are a profitable customer. Such a determination may be based on the amount of money the consumer spends on the card, the degree to which they pay their bill, or other such measures. In these cases, the communications for such a user may not be used to formulate the offer profile for the credit card until such information can be amassed by the card issuer.
Continuing with the example above, a third user, user C 518, has previously sent or received twenty emails and forty instant messages, collectively 520. According to the method 300 described above, the profile server 110 builds, or has previously built a consumer profile 522 based on the content of the sixty communications 520 associated with user C 518. Further, offers 1, 510, and 2, 512, have been associated with a particular page of Web content such that the web server 104 will include either offer 1, 510, or offer 2, 512, but not both, when a user requests the page. When user C 518 requests the page of content with which offers 1, 510, and 2, 512, are associated, a comparison engine 524 compares the consumer profile 522 for user C 518 with the offer profile 514 for offer 1, 510, and the offer profile 516 for offer 2, 512. If, for example, the offer profile 514 for offer 1, 510, which is based at least in part on the content of communications (504 and 508) from user A 502 and user B 506, is a closer match that the offer profile 516 for offer 2, 512, which is based only on the content of communications 508 from user B 506, offer 1, 510, is included on the page of content and sent to the client 102 a. As user A 502, user B 506, and user C 518 send additional communications such as emails and/or instant messages, the new communications are added to the collections for each user, collection 504, collection 508, and collection 520, for users A 502, B 506, and C 518, respectively. As the collections grow, the consumer profiles (profile 522 for user C 518, for example) become more reflective of which offers are more likely to be shown to a user. In addition, as user A 502 and user B 506 generate additional communications, their communications are added to the collections of communications associated with offers to which they have responded.
As a further illustration of the example above, user B 506 may be an avid golfer, and the work “golf” may appear in the collection of communications 508 associated with user B 506 more often than the word “golf” appears in a typical collection of communications from a random member of the general public. Therefore, using the example above, when user B 506 responds to offer 2, 512, the word “golf” will appear in the collection of communications for offer 2 512 and would impact the offer profile 516 for offer 2, 512. However, because user A 502 did not respond to offer 2, 512, and the collection of communications 504 from user A 502 does not contain the word “golf” any more frequently than would a set of communications from a member of the general public, the collection of communications for offer 1, 514, does not contain the word “golf” as frequently as does the collection of communications (504 and 508) for offer 2, 512.
Further, user C 518 may also be an avid golfer, and therefore the collection of communications 520 associated with user C 518 may contain the word “golf” more frequently than would a collection of communications associated with a member of the general public. As described above, the consumer profile 522 for user C 518 would reflect her above-average use of the word “golf” in her communications. Therefore, when user C 518 requests a page of web content to which offers 1 and 2 (510 and 512 respectively) are assigned, the comparison engine 524 recognizes a similarity between the offer profile 516 for offer 2, 512 and the consumer profile 522 for user C, 518, and therefore offer 2 512 is shown to user C 518.
Continuing with the above example, the receiver 610 transmits information about the first user's interaction with an offer to the database server 604, and to a first profiler 612. The first profiler 612 then requests the communications associated with the first user, as well as the communications associated with other users who also responded to the same offer, from the database server 604. The first profiler 612 then creates an offer profile based at least in part on the cumulative content of the communications associated with the set of users, including the first user, who responded to the offer. The first profiler 612 then sends the offer profile to the database server 604 where it is stored for future use.
A second user then logs into the system 600 from a client 102 b and generates electronic communications such as emails, instant messages, or similar messages which are stored in the database server, 604. A second profiler 614 requests the communications associated with the second user and creates a consumer profile for the second user based at least in part on the cumulative content of the communications associated with the second user and sends the consumer profile for the second user to the database server 604. When the second user requests a page of Web content having multiple offers associated with it, a comparison engine 616 requests the offer profiles for the associated offers and the consumer profile for the second user from the database server 604. The comparison engine 616 then determines which offer is most relevant to the second user based on the similarity between the second user's consumer profile and the offer profiles.
For embodiments in which the invention is provided as software, the program may be written in any one of a number of high level languages such as FORTRAN, PASCAL, JAVA, C, C++, or BASIC. Additionally, the software could be implemented in an assembly language directed to the microprocessor resident on the target computer, for example, the software could be implemented in Intel 80x86 assembly language if it were configured to run on an IBM PC or PC clone. The software may be embodied on an article of manufacture including, but not limited to, a floppy disk, a hard disk, an optical disk, a magnetic tape, a PROM, an EPROM, EEPROM, field-programmable gate array, or CD-ROM.
In these embodiments, the software may be configured to run on any personal-type computer or workstation such as a PC or PC-compatible machine, an Apple Macintosh, a Sun workstation, etc. In general, any device could be used as long as it is able to perform all of the functions and capabilities described herein. The particular type of computer or workstation is not central to the invention.
The computer typically will include a central processor, a main memory unit for storing programs and/or data, an input/output (I/O) controller, a display device, and a data bus coupling these components to allow communication therebetween. The memory includes random access memory (RAM) and read only memory (ROM) chips. The computer typically also has one or more input devices such as a keyboard (e.g., an alphanumeric keyboard and/or a musical keyboard), a mouse, and, in some embodiments, a joystick.
The computer typically also has a hard drive with hard disks therein and a floppy drive for receiving floppy disks such as 3.5 inch disks. Other devices also can be part of the computer including output devices (e.g., printer or plotter) and/or optical disk drives for receiving, writing and reading digital data on a CD-ROM. In the disclosed embodiment, one or more computer programs define the operational capabilities of the system, as mentioned previously. These programs can be loaded onto the hard drive and/or into the memory of the computer via the floppy drive, CD-ROM, or other like device. In general, the controlling software program(s) and all of the data utilized by the program(s) are stored on one or more of the computer's storage mediums such as the hard drive, CD-ROM, etc. In general, the programs implement the invention on the computer, and the programs either contain or access the data needed to implement all of the functionality of the invention on the computer.
Variations, modifications, and other implementations of what is described herein may occur to those of ordinary skill without departing from the spirit and scope of the invention. Accordingly, the invention is not to be defined only by the preceding illustrative description.
|US4775935||22. Sept. 1986||4. Okt. 1988||Westinghouse Electric Corp.||Video merchandising system with variable and adoptive product sequence presentation order|
|US4833308||24. Juli 1986||23. Mai 1989||Advance Promotion Technologies, Inc.||Checkout counter product promotion system and method|
|US5155591||23. Okt. 1989||13. Okt. 1992||General Instrument Corporation||Method and apparatus for providing demographically targeted television commercials|
|US5401946||22. Juli 1991||28. März 1995||Weinblatt; Lee S.||Technique for correlating purchasing behavior of a consumer to advertisements|
|US5592560||8. Sept. 1994||7. Jan. 1997||Credit Verification Corporation||Method and system for building a database and performing marketing based upon prior shopping history|
|US5636346||9. Mai 1994||3. Juni 1997||The Electronic Address, Inc.||Method and system for selectively targeting advertisements and programming|
|US5677853||16. Nov. 1994||14. Okt. 1997||Delco Electronics Corp.||Product testing by statistical profile of test variables|
|US5710884||29. März 1995||20. Jan. 1998||Intel Corporation||System for automatically updating personal profile server with updates to additional user information gathered from monitoring user's electronic consuming habits generated on computer during use|
|US5710887||29. Aug. 1995||20. Jan. 1998||Broadvision||Computer system and method for electronic commerce|
|US5717923 *||3. Nov. 1994||10. Febr. 1998||Intel Corporation||Method and apparatus for dynamically customizing electronic information to individual end users|
|US5721827||2. Okt. 1996||24. Febr. 1998||James Logan||System for electrically distributing personalized information|
|US5724521||3. Nov. 1994||3. März 1998||Intel Corporation||Method and apparatus for providing electronic advertisements to end users in a consumer best-fit pricing manner|
|US5740549||12. Juni 1995||14. Apr. 1998||Pointcast, Inc.||Information and advertising distribution system and method|
|US5774170||13. Dez. 1994||30. Juni 1998||Hite; Kenneth C.||System and method for delivering targeted advertisements to consumers|
|US5790426||30. Apr. 1997||4. Aug. 1998||Athenium L.L.C.||Automated collaborative filtering system|
|US5848396||26. Apr. 1996||8. Dez. 1998||Freedom Of Information, Inc.||Method and apparatus for determining behavioral profile of a computer user|
|US5855482||22. Mai 1996||5. Jan. 1999||Island Graphics Corporation||Graphic visualization of consumer desirability hierarchy|
|US5867799||4. Apr. 1996||2. Febr. 1999||Lang; Andrew K.||Information system and method for filtering a massive flow of information entities to meet user information classification needs|
|US5915243||29. Aug. 1996||22. Juni 1999||Smolen; Daniel T.||Method and apparatus for delivering consumer promotions|
|US5918014||26. Dez. 1996||29. Juni 1999||Athenium, L.L.C.||Automated collaborative filtering in world wide web advertising|
|US5933811||20. Aug. 1996||3. Aug. 1999||Paul D. Angles||System and method for delivering customized advertisements within interactive communication systems|
|US5948061||29. Okt. 1996||7. Sept. 1999||Double Click, Inc.||Method of delivery, targeting, and measuring advertising over networks|
|US5959623||8. Dez. 1995||28. Sept. 1999||Sun Microsystems, Inc.||System and method for displaying user selected set of advertisements|
|US5983214||5. Nov. 1998||9. Nov. 1999||Lycos, Inc.||System and method employing individual user content-based data and user collaborative feedback data to evaluate the content of an information entity in a large information communication network|
|US6009410||16. Okt. 1997||28. Dez. 1999||At&T Corporation||Method and system for presenting customized advertising to a user on the world wide web|
|US6026370||28. Aug. 1997||15. Febr. 2000||Catalina Marketing International, Inc.||Method and apparatus for generating purchase incentive mailing based on prior purchase history|
|US6041311||28. Jan. 1997||21. März 2000||Microsoft Corporation||Method and apparatus for item recommendation using automated collaborative filtering|
|US6044376||24. Apr. 1997||28. März 2000||Imgis, Inc.||Content stream analysis|
|US6112186||31. März 1997||29. Aug. 2000||Microsoft Corporation||Distributed system for facilitating exchange of user information and opinion using automated collaborative filtering|
|US6119098||14. Okt. 1997||12. Sept. 2000||Patrice D. Guyot||System and method for targeting and distributing advertisements over a distributed network|
|US6134532 *||14. Nov. 1997||17. Okt. 2000||Aptex Software, Inc.||System and method for optimal adaptive matching of users to most relevant entity and information in real-time|
|US6144944||22. Apr. 1998||7. Nov. 2000||Imgis, Inc.||Computer system for efficiently selecting and providing information|
|US6157921 *||1. Mai 1999||5. Dez. 2000||Barnhill Technologies, Llc||Enhancing knowledge discovery using support vector machines in a distributed network environment|
|US6183366 *||26. Juni 1998||6. Febr. 2001||Sheldon Goldberg||Network gaming system|
|US6195657||25. Sept. 1997||27. Febr. 2001||Imana, Inc.||Software, method and apparatus for efficient categorization and recommendation of subjects according to multidimensional semantics|
|US6216129||12. März 1999||10. Apr. 2001||Expanse Networks, Inc.||Advertisement selection system supporting discretionary target market characteristics|
|US6236978||14. Nov. 1997||22. Mai 2001||New York University||System and method for dynamic profiling of users in one-to-one applications|
|US6256633||25. Juni 1998||3. Juli 2001||U.S. Philips Corporation||Context-based and user-profile driven information retrieval|
|US6298228||12. Nov. 1998||2. Okt. 2001||Ericsson Inc.||Lazy updates of profiles in a system of communication devices|
|US6298348||12. März 1999||2. Okt. 2001||Expanse Networks, Inc.||Consumer profiling system|
|US6308175||19. Nov. 1998||23. Okt. 2001||Lycos, Inc.||Integrated collaborative/content-based filter structure employing selectively shared, content-based profile data to evaluate information entities in a massive information network|
|US6324519||12. März 1999||27. Nov. 2001||Expanse Networks, Inc.||Advertisement auction system|
|US6327574||1. Febr. 1999||4. Dez. 2001||Encirq Corporation||Hierarchical models of consumer attributes for targeting content in a privacy-preserving manner|
|US6334127||17. Juli 1998||25. Dez. 2001||Net Perceptions, Inc.||System, method and article of manufacture for making serendipity-weighted recommendations to a user|
|US6339761||13. Mai 1999||15. Jan. 2002||Hugh V. Cottingham||Internet service provider advertising system|
|US6345293||3. Juli 1997||5. Febr. 2002||Microsoft Corporation||Personalized information for an end user transmitted over a computer network|
|US6356879||9. Okt. 1998||12. März 2002||International Business Machines Corporation||Content based method for product-peer filtering|
|US6370514||2. Aug. 1999||9. Apr. 2002||Marc A. Messner||Method for marketing and redeeming vouchers for use in online purchases|
|US6370526||18. Mai 1999||9. Apr. 2002||International Business Machines Corporation||Self-adaptive method and system for providing a user-preferred ranking order of object sets|
|US6385592||30. Juni 1999||7. Mai 2002||Big Media, Inc.||System and method for delivering customized advertisements within interactive communication systems|
|US6460036||5. Dez. 1997||1. Okt. 2002||Pinpoint Incorporated||System and method for providing customized electronic newspapers and target advertisements|
|US6480885||25. Apr. 2000||12. Nov. 2002||Michael Olivier||Dynamically matching users for group communications based on a threshold degree of matching of sender and recipient predetermined acceptance criteria|
|US6484148||19. Febr. 2000||19. Nov. 2002||John E. Boyd||Electronic advertising device and method of using the same|
|US6487539||6. Aug. 1999||26. Nov. 2002||International Business Machines Corporation||Semantic based collaborative filtering|
|US6513052||15. Dez. 1999||28. Jan. 2003||Imation Corp.||Targeted advertising over global computer networks|
|US6519571||27. Mai 1999||11. Febr. 2003||Accenture Llp||Dynamic customer profile management|
|US6560578 *||31. Jan. 2001||6. Mai 2003||Expanse Networks, Inc.||Advertisement selection system supporting discretionary target market characteristics|
|US6631372 *||12. Febr. 1999||7. Okt. 2003||Yahoo! Inc.||Search engine using sales and revenue to weight search results|
|US6836773 *||27. Sept. 2001||28. Dez. 2004||Oracle International Corporation||Enterprise web mining system and method|
|US6968333 *||2. Apr. 2001||22. Nov. 2005||Tangis Corporation||Soliciting information based on a computer user's context|
|US7062510 *||2. Dez. 1999||13. Juni 2006||Prime Research Alliance E., Inc.||Consumer profiling and advertisement selection system|
|US7113917 *||7. Mai 2001||26. Sept. 2006||Amazon.Com, Inc.||Personalized recommendations of items represented within a database|
|US7149704 *||25. Jan. 2002||12. Dez. 2006||Claria Corporation||System, method and computer program product for collecting information about a network user|
|US7158943 *||4. Sept. 2002||2. Jan. 2007||Ramon Van Der Riet||Marketing communication and transaction/distribution services platform for building and managing personalized customer relationships|
|US7174305 *||23. Jan. 2001||6. Febr. 2007||Opentv, Inc.||Method and system for scheduling online targeted content delivery|
|US7181438 *||30. Mai 2000||20. Febr. 2007||Alberti Anemometer, Llc||Database access system|
|US7272573 *||13. Nov. 2001||18. Sept. 2007||International Business Machines Corporation||Internet strategic brand weighting factor|
|US7386439 *||4. Febr. 2003||10. Juni 2008||Cataphora, Inc.||Data mining by retrieving causally-related documents not individually satisfying search criteria used|
|US20020042739||13. März 2001||11. Apr. 2002||Kannan Srinivasan||Method and system for creating and administering internet marketing promotions|
|US20020049704 *||27. Apr. 2001||25. Apr. 2002||Vanderveldt Ingrid V.||Method and system for dynamic data-mining and on-line communication of customized information|
|US20020059574||17. Mai 2001||16. Mai 2002||Tudor Geoffrey T.||Method and apparatus for management and delivery of electronic content to end users|
|US20020062368||1. März 2001||23. Mai 2002||David Holtzman||System and method for establishing and evaluating cross community identities in electronic forums|
|US20020065802 *||30. Mai 2001||30. Mai 2002||Koki Uchiyama||Distributed monitoring system providing knowledge services|
|US20020099730||14. Mai 2001||25. Juli 2002||Applied Psychology Research Limited||Automatic text classification system|
|US20020107853||26. Juli 2001||8. Aug. 2002||Recommind Inc.||System and method for personalized search, information filtering, and for generating recommendations utilizing statistical latent class models|
|US20020130902||16. März 2001||19. Sept. 2002||International Business Machines Corporation||Method and apparatus for tailoring content of information delivered over the internet|
|US20020133404||19. März 2001||19. Sept. 2002||Pedersen Brad D.||Internet advertisements having personalized context|
|US20020161664||17. Okt. 2001||31. Okt. 2002||Shaya Steven A.||Intelligent performance-based product recommendation system|
|US20020186867||11. Juni 2001||12. Dez. 2002||Philips Electronics North America Corp.||Filtering of recommendations employing personal characteristics of users|
|US20020198882||15. Jan. 2002||26. Dez. 2002||Linden Gregory D.||Content personalization based on actions performed during a current browsing session|
|US20030037041||1. Okt. 2002||20. Febr. 2003||Pinpoint Incorporated||System for automatic determination of customized prices and promotions|
|US20030158777 *||26. Juli 2001||21. Aug. 2003||Eyal Schiff||User-driven data network communication system and method|
|1||"Cisco Unveils Next Phase of Cisco 7000 Family of Internet Routers" [online]. [Retrieved on Jul. 22, 2003]. Retrieved from the Internet: http://www.tmcnet.com/enews/091201a.htm.|
|2||"CYBERsitter Spam Manager Introduction and Overview of Operations" [online]. Retrieved from the Internet: http://www.noxmail.com/imail/spammanagerimail.pdf.|
|3||"Microlanguage Technology" [online]. [Retrieved on Jul. 22, 2003]. Retrieved from the Internet: http://www.microlanguage.com/prod-technology.html.|
|4||"Technology Introduction" [online]. [Retrieved on Apr. 22, 2003 and May 9, 2003]. Retrieved from the Internet: http://www.automony.com/Content/Technology/.|
|5||"Microlanguage Technology" [online]. [Retrieved on Jul. 22, 2003]. Retrieved from the Internet: http://www.microlanguage.com/prod—technology.html.|
|6||Hinton, Craig, "iCognito PureSight," SC Magazine, Mar. 2003. Retrieved from the internet: http://www.incognito.com/pdf/sc-puresight-bestbuy.pdf.|
|7||Hinton, Craig, "iCognito PureSight," SC Magazine, Mar. 2003. Retrieved from the internet: http://www.incognito.com/pdf/sc—puresight—bestbuy.pdf.|
|Zitiert von Patent||Eingetragen||Veröffentlichungsdatum||Antragsteller||Titel|
|US20140207544 *||23. Jan. 2013||24. Juli 2014||Visan, Inc.||Ranking limited time discounts or deals|
|USD735225||3. Jan. 2013||28. Juli 2015||Par8O, Inc.||Display screen of a computing device with graphical user interface|
|Internationale Klassifikation||G06Q30/00, G06F15/16, G06F17/00|
|22. Juni 2012||AS||Assignment|
Owner name: DROPBOX, INC., CALIFORNIA
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:GRAHAM, PAUL;REEL/FRAME:028426/0760
Effective date: 20120621
|16. Nov. 2012||AS||Assignment|
Owner name: MORGAN STANLEY SENIOR FUNDING, INC., NEW YORK
Free format text: PATENT SECURITY AGREEMENT;ASSIGNOR:DROPBOX, INC.;REEL/FRAME:029310/0864
Effective date: 20121024
|21. März 2014||AS||Assignment|
Owner name: DROPBOX, INC., CALIFORNIA
Free format text: RELEASE OF SECURITY AGREEMENT;ASSIGNOR:MORGAN STANLEY SENIOR FUNDING, INC., AS COLLATERAL AGENT;REEL/FRAME:032492/0676
Effective date: 20140320
|24. März 2014||AS||Assignment|
Owner name: JPMORGAN CHASE BANK, N.A., AS COLLATERAL AGENT, NE
Free format text: SECURITY INTEREST;ASSIGNOR:DROPBOX, INC.;REEL/FRAME:032510/0890
Effective date: 20140320
|17. Nov. 2016||FPAY||Fee payment|
Year of fee payment: 4
|14. Apr. 2017||AS||Assignment|
Owner name: JPMORGAN CHASE BANK, N.A., AS COLLATERAL AGENT, NE
Free format text: SECURITY INTEREST;ASSIGNOR:DROPBOX, INC.;REEL/FRAME:042254/0001
Effective date: 20170403